Wednesday, August 28, 2013

A paper published today in Nature Climate Change finds climate models have greatly exaggerated global warming over the past 20 years, noting the observed warming is "less than half" of the modeled warming. The authors falsify the models at a confidence level of 90%, and also find that there has been no statistically significant global warming for the past 20 years. According to the authors, "The evidence, therefore, indicates that the current generation of climate models ...do not reproduce the observed global warming over the past 20 years, or the slowdown in global warming over the past fifteen years." The paper follows another recent paper falsifying climate models at a confidence level of greater than 98% for the past 15 years.

In terms of reasons for model underestimation, the apparent ‘preferred’ explanation of ‘the ocean ate it’ does not get any play here, other than in context of a brief consideration of natural internal variability. Their conclusion This difference might be explained by some combination of errors in external forcing, model response and internal [natural] climate variability is right on the money IMO, although I don’t think their analysis of why the models might be wrong was particularly illuminating. If you would like further illumination on why the climate models might be wrong, I refer you to my uncertainty monster paper.

Article tools

Recent observed global warming is significantly less than that simulated by climate models. This difference might be explained by some combination of errors in external forcing, model response and internal [natural] climate variability.

At a glance

Global mean surface temperature over the past 20 years (1993–2012) rose at a rate of 0.14 ± 0.06 °C per decade (95% confidence interval)1. This rate of warming is significantly slower than that simulated by the climate models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). To illustrate this, we considered trends in global mean surface temperature computed from 117 simulations of the climate by 37 CMIP5 models (see Supplementary Information). These models generally simulate natural variability — including that associated with the El Niño–Southern Oscillation and explosive volcanic eruptions — as well as estimate the combined response of climate to changes in greenhouse gas concentrations, aerosol abundance (of sulphate, black carbon and organic carbon, for example), ozone concentrations (tropospheric and stratospheric), land use (for example, deforestation) and solar variability. By averaging simulated temperatures only at locations where corresponding observations exist, we find an average simulated rise in global mean surface temperature of 0.30 ± 0.02 °C per decade (using 95% confidence intervals on the model average). The observed rate of warming given above is less than half of this simulated rate, and only a few simulations provide warming trends within the range of observational uncertainty (Fig. 1a)

Figure 1: Trends in global mean surface temperature.

a, 1993–2012. b, 1998–2012. Histograms of observed trends (red hatching) are from 100 reconstructions of the HadCRUT4 dataset1. Histograms of model trends (grey bars) are based on 117 simulations of the models, and black curves are smoothed versions of the model trends. The ranges of observed trends reflect observational uncertainty, whereas the ranges of model trends reflect forcing uncertainty, as well as differences in individual model responses to external forcings and uncertainty arising from internal climate variability.

The inconsistency between observed and simulated global warming is even more striking for temperature trends computed over the past fifteen years (1998–2012). For this period, the observed trend of 0.05 ± 0.08 °C per decade is more than four times smaller than the average simulated trend of 0.21 ± 0.03 °C per decade (Fig. 1b). It is worth noting that the observed trend over this period — not significantly different from zero — suggests a temporary 'hiatus' in global warming2, 3, 4. The divergence between observed and CMIP5-simulated global warming begins in the early 1990s, as can be seen when comparing observed and simulated running trends from 1970–2012 (Fig. 2a and 2b for 20-year and 15-year running trends, respectively).

Figure 2: Global mean surface temperature trends and p values.

a,b, 20-year (a) and 15-year (b) running trends. Black curves are ensemble-averaged trends over the 37 sets of model simulations. Dark-grey shading indicates the 2.5–97.5% ranges of the simulated estimates. Light-grey shading shows the 95% uncertainty ranges of the ensemble means, derived by dividing the 2.5–97.5% ranges by the square root of the number of models. Red curves are the observed trends averaged over 100 realizations and the horizontal red lines show the observed 1900–2012 trends averaged over 100 realizations. Black cross-hatchings are the 95% uncertainty ranges for simulated 1900–2012 ensemble mean trends. Note that the observed and simulated long-term trends are very similar to one another, and are smaller than the short-term trends. c,d, 20-year (c) and 15-year (d) p values on trend differences between the simulations and observations for assumption (1) (purple curves), or assumption (2) (green curves). The horizontal dashed lines indicate the threshold below which we reject the null hypothesis.

The evidence, therefore, indicates that the current generation of climate models (when run as a group, with the CMIP5 prescribed forcings) do not reproduce the observed global warming over the past 20 years, or the slowdown in global warming over the past fifteen years. This interpretation is supported by statistical tests of the null hypothesis that the observed and model mean trends are equal, assuming that either: (1) the models are exchangeable with each other (that is, the 'truth plus error' view); or (2) the models are exchangeable with each other and with the observations (seeSupplementary Information). Differences between observed and simulated 20-year trends have p values (Supplementary Information) that drop to close to zero by 1993–2012 under assumption (1) and to 0.04 under assumption (2) (Fig. 2c). Here we note that the smaller the p value is, the stronger the evidence against the null hypothesis. On this basis, the rarity of the 1993–2012 trend difference under assumption (1) is obvious. Under assumption (2), this implies that such an inconsistency is only expected to occur by chance once in 500 years, if 20-year periods are considered statistically independent. Similar results apply to trends for 1998–2012 (Fig. 2d). In conclusion, we reject the null hypothesis that the observed and model mean trends are equal at the 10% level.

One possible explanation for the discrepancy is that forced and internal variation might combine differently in observations than in models. For example, the forced trends in models are modulated up and down by simulated sequences of ENSO events, which are not expected to coincide with the observed sequence of such events. For this reason the moderating influence on global warming that arises from the decay of the 1998 El Niño event does not occur in the models at that time. Thus we employ here an established technique to estimate the impact of ENSO on global mean temperature, and to incorporate the effects of dynamically induced atmospheric variability and major explosive volcanic eruptions5, 6. Although these three natural variations account for some differences between simulated and observed global warming, these differences do not substantively change our conclusion that observed and simulated global warming are not in agreement over the past two decades (Fig. 3). Another source of internal climate variability that may contribute to the inconsistency is the Atlantic multidecadal oscillation7 (AMO). However, this is difficult to assess as the observed and simulated variations in global temperature that are associated with the AMO seem to be dominated by a large and concurrent signal of presumed anthropogenic origin (Supplementary Fig. S1). It is worth noting that in any case the AMO has not driven cooling over the past 20 years.

Figure 3: Trends in global mean surface temperature and in associated natural and residual time series.

a, 1993–2012. b, 1998–2012. The 2.5–97.5% ranges for observed estimates are shown by the red boxes. The 2.5–97.5% ranges for simulated estimates are represented by the open black boxes, with the 95% ranges on ensemble mean trends indicated by grey shading. The estimated natural signals shown are associated with the El Niño-Southern Oscillation (ENSO), dynamically induced atmospheric variability (cold ocean–warm Earth; COWL) and major explosive volcanic eruptions (Volcano). Trends in global mean surface temperature without these estimated natural signals are shown at the bottom (Residual).

Another possible driver of the difference between observed and simulated global warming is increasing stratospheric aerosol concentrations. Results from several independent datasets show that stratospheric aerosol abundance has increased since the late 1990s, owing to a series of comparatively small tropical volcanic eruptions8. Although none of the CMIP5 simulations take this into account, two independent sets of model simulations estimate that increasing stratospheric aerosols have had a surface cooling impact of about 0.07 °C per decade since 19988, 9. If the CMIP5 models had accounted for increasing stratospheric aerosol, and had responded with the same surface cooling impact, the simulations and observations would be in closer agreement. Other factors that contribute to the discrepancy could include a missing decrease in stratospheric water vapour10 (whose processes are not well represented in current climate models), errors in aerosol forcing in the CMIP5 models, a bias in the prescribed solar irradiance trend, the possibility that the transient climate sensitivity of the CMIP5 models could be on average too high11, 12 or a possible unusual episode of internal climate variability not considered above13, 14. Ultimately the causes of this inconsistency will only be understood after careful comparison of simulated internal climate variability and climate model forcings with observations from the past two decades, and by waiting to see how global temperature responds over the coming decades.